Accelerator Cohort Social Network Structure and Startup Performance

Startup accelerators have become a widespread means of supporting entrepreneurs and their ventures. Yet, deductive empirical research explaining and predicting peer effects in startup accelerator cohorts is wanting. Following cues from the theoretical and empirical literatures, we hypothesize and find that higher performing cohorts have more connections between peer startups. This suggests that beyond merely acting as brokers, accelerators enable potentially valuable peer effects. However, as the dark side of density literature suggests, we also hypothesize an inverse curvilinear relationship between cohort peer network density and likelihood that a cohort will produce a unicorn. In this research, we measure the performance of the Startup Accelerator cohort in terms of the number of startups with high valuations in the cohort. High valuation startups were identified by Techstars on their 2019 Top 50 startups list featuring graduates from their accelerator. We approximate the intra-cohort social network structures of 1537 startups comprising 154 Techstars cohorts using their activity on Twitter. We find that there exists an upper limit to the impact of cohort network density on performance, after which any further increase becomes negative.